![]() Savaşların hisse senedi piyasaları üzerindeki etkileri akademisyenler, yatırımcılar, portföy yöneticileri, politika yapıcılar şeklindeki taraflar için oldukça önemlidir. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. ![]() ![]() As the pandemic led to great collateral damage or process disruption to a variety of organizations, including banks (Shahabi et al., 2021), airlines Milne et al., 2020Milne et al.,, 2021, ambulatory endoscopy centres (Das, 2020), heart clinics (Zeinalnezhad et al., 2020), laboratories (Lim et al., 2020) and outpatient dialysis services (Allen, Bhanji, et al., 2020), necessary countermeasures were adopted to lower the risk of transmission and to improve effectiveness of these measures. Twenty papers explored the other sectors at the national and regional levels, including industrial network (Song et al., 2020), tourism Luo et al., 2021), national security (Prikazchikov et al., 2021), food-energy-water (Calder et al., 2021), economy Fosco & Zurita, 2021 Inoue et al., 2021 Inoue & Todo, 2020 Sharma et al., 2021), financial (Spelta et al., 2021), social activity (de Brito Jr et al., 2021 Schmidt & Albert, 2021 Weibrecht et al., 2021), healthcare, employment (Marreros et al., 2021) and transport and land-use (Habib & Anik, 2021). Ten papers investigated the disruptions and uncertainties to the supply chain caused by the COVID-19 pandemic (Achmad et al., 2021 Burgos & Ivanov, 2021 Choudhary et al., 2021 Duan et al., 2021 Ghadge et al., 2021 Moosavi & Hosseini, 2021 Nguyen, 2021 Sinha et al., 2020) and the post-pandemic recovery strategies (Ivanov, 2021 Rahman et al., 2021). Empirical implications based on robust estimates are further illustrated. In contrast, robust VAR models provide more reliable results, the validity of which is verified via various approaches. Our baseline results suggest that the traditional VAR model may significantly overestimate some parameters, as well as IRF and FEVD metrics. Our sample covers July 2017–June 2020, which includes the history-writing price drop of WTI on April 20, 2020. Our empirical results include logged daily realized volatilities of six common safe haven assets: futures of gold, silver, Brent oil and West Texas Intermediate (WTI) oil and currencies of Swiss Francs and Japanese Yen. Via extensive simulation studies, we show that the robust VAR models lead to much more accurate estimates than the original VAR in the presence of outliers. The robust information criteria are also proposed to select the appropriate number of temporal lags. To handle potential outliers in multivariate time series, this paper investigates two robust estimation methods of the VAR model, the reweighted multivariate least trimmed squares and the multivariate MM-estimation. Despite its usefulness in providing associated metrics such as the impulse response function (IRF) and forecast error variance decomposition (FEVD), the traditional VAR model estimated via the usual ordinary least squares is vulnerable to outliers. ![]() The vector autoregressive (VAR) model has been popularly employed in operational practice to study multivariate time series.
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